What are the trade-offs in AI interpretability?

What are the trade-offs in AI interpretability?

The trade-offs in AI interpretability often involve balancing model accuracy with transparency. More complex models may perform better but are harder to interpret.

Can you give an example of a complex model?

Sure! Deep learning models, like neural networks, are complex and can achieve high accuracy, but they often act as 'black boxes'.

Why is interpretability important?

Interpretability is crucial for trust, accountability, and understanding model decisions, especially in sensitive areas like healthcare.

Are there methods to improve interpretability?

Yes, techniques like LIME and SHAP can help explain model predictions by approximating the model locally.

What are the risks of using less interpretable models?

Using less interpretable models can lead to misuse, lack of accountability, and difficulty in diagnosing errors.